





'''simple docstring'''



import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE       =     argparse.ArgumentParser()

	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
	)
	# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
	parser.add_argument(
	    '''--original_config_file''',
	    default=None,
	    type=str,
	    help='''The YAML config file corresponding to the original architecture.''',
	)
	parser.add_argument(
	    '''--num_in_channels''',
	    default=None,
	    type=int,
	    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
	)
	parser.add_argument(
	    '''--scheduler_type''',
	    default='''pndm''',
	    type=str,
	    help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
	)
	parser.add_argument(
	    '''--pipeline_type''',
	    default=None,
	    type=str,
	    help=(
	        '''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
	        '''. If `None` pipeline will be automatically inferred.'''
	    ),
	)
	parser.add_argument(
	    '''--image_size''',
	    default=None,
	    type=int,
	    help=(
	        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
	        ''' Base. Use 768 for Stable Diffusion v2.'''
	    ),
	)
	parser.add_argument(
	    '''--prediction_type''',
	    default=None,
	    type=str,
	    help=(
	        '''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
	        ''' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'''
	    ),
	)
	parser.add_argument(
	    '''--extract_ema''',
	    action='''store_true''',
	    help=(
	        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
	        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
	        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
	    ),
	)
	parser.add_argument(
	    '''--upcast_attention''',
	    action='''store_true''',
	    help=(
	        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
	        ''' diffusion 2.1.'''
	    ),
	)
	parser.add_argument(
	    '''--from_safetensors''',
	    action='''store_true''',
	    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
	)
	parser.add_argument(
	    '''--to_safetensors''',
	    action='''store_true''',
	    help='''Whether to store pipeline in safetensors format or not.''',
	)
	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
	parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
	parser.add_argument(
	    '''--stable_unclip''',
	    type=str,
	    default=None,
	    required=False,
	    help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
	)
	parser.add_argument(
	    '''--stable_unclip_prior''',
	    type=str,
	    default=None,
	    required=False,
	    help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
	)
	parser.add_argument(
	    '''--clip_stats_path''',
	    type=str,
	    help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
	    required=False,
	)
	parser.add_argument(
	    '''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
	)
	parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
	parser.add_argument(
	    '''--vae_path''',
	    type=str,
	    default=None,
	    required=False,
	    help='''Set to a path, hub id to an already converted vae to not convert it again.''',
	)
	_SCREAMING_SNAKE_CASE       =     parser.parse_args()

	_SCREAMING_SNAKE_CASE       =     download_from_original_stable_diffusion_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    prediction_type=args.prediction_type,
	    model_type=args.pipeline_type,
	    extract_ema=args.extract_ema,
	    scheduler_type=args.scheduler_type,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    stable_unclip=args.stable_unclip,
	    stable_unclip_prior=args.stable_unclip_prior,
	    clip_stats_path=args.clip_stats_path,
	    controlnet=args.controlnet,
	    vae_path=args.vae_path,
	)

	if args.half:
		pipe.to(torch_dtype=torch.floataa)

	if args.controlnet:
		# only save the controlnet model
		pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
	else:
		pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)










'''simple docstring'''

from __future__ import annotations

SCREAMING_SNAKE_CASE__     :						List[Any]	=  1.6021e-19  # units = C






def 				a  (     UpperCamelCase_    :    float      ,					UpperCamelCase_    :    float      ,					UpperCamelCase_    :    float      ,					)       ->	tuple[str, float]:
		if (conductivity, electron_conc, mobility).count(0			) != 1:
				raise ValueError('You cannot supply more or less than 2 values'			)
		elif conductivity < 0:
				raise ValueError('Conductivity cannot be negative'			)
		elif electron_conc < 0:
				raise ValueError('Electron concentration cannot be negative'			)
		elif mobility < 0:
				raise ValueError('mobility cannot be negative'			)
		elif conductivity == 0:
				return (
				    "conductivity",
				    mobility * electron_conc * ELECTRON_CHARGE,
				)
		elif electron_conc == 0:
				return (
				    "electron_conc",
				    conductivity / (mobility * ELECTRON_CHARGE),
				)
		else:
				return (
				    "mobility",
				    conductivity / (electron_conc * ELECTRON_CHARGE),
				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




